\subsection{recommendations}
These recommendations are for BNP Paribas. We believe that we have shown GPGPUs to be a great fit for the these types of algorithms. GPGPUs can take the bottleneck portion of the algorithm and greatly accelerate them. This thesis tries to present some general guidelines and a logical how to in programming GPGPUs for these kinds of algorithms. We believe that for a software developer trained in C++, the GPGPU programming language is easy to pick up and can be used in the object oriented environment.

We hope that you will integrate this technology in your offices soon and would like to offer our assistance. We are availble to assist you in selecting the right cards, building the software, or training your developers. 

We see potential for the future of GPGPUs to accelerate various types of algorithms. The algorithm used in this thesis comes from a relatively new financial distribution and there have been various new works published in the last 2 years describing new versions of the distribution. The newer versions have more parameters and will take even more time to fit. We believe GPGPUs are the perfect solution in these cases.
 

